




"""simple docstring"""






from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
			import timm


if is_torch_available():
			from torch import Tensor






class lowerCAmelCase__      (	lowercase,      lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """pixel_values"""
			lowerCamelCase__ =     False
			lowerCamelCase__ =     TimmBackboneConfig
			def __init__(    self    , lowercase    , **lowercase    ):
							requires_backends(self    , 'timm'    )
							super().__init__(lowercase    )
							_lowerCamelCase	:     Dict       =       config

							if config.backbone is None:
											raise ValueError('backbone is not set in the config. Please set it to a timm model name.'    )

							if config.backbone not in timm.list_models():
											raise ValueError(F'''backbone {config.backbone} is not supported by timm.'''    )

							if hasattr(lowercase    , 'out_features'    ) and config.out_features is not None:
											raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'    )

							_lowerCamelCase	:     Optional[int]       =       getattr(lowercase    , 'use_pretrained_backbone'    , lowercase    )
							if pretrained is None:
											raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.'    )

							# We just take the final layer by default. This matches the default for the transformers models.
							_lowerCamelCase	:     List[str]       =       config.out_indices if getattr(lowercase    , 'out_indices'    , lowercase    ) is not None else (-1,)

							_lowerCamelCase	:     Tuple       =       timm.create_model(
							    config.backbone    , pretrained=lowercase    , features_only=config.features_only    , in_chans=config.num_channels    , out_indices=lowercase    , **lowercase    , )
							# These are used to control the output of the model when called. If output_hidden_states is True, then
							# return_layers is modified to include all layers.
							_lowerCamelCase	:     Tuple       =       self._backbone.return_layers
							_lowerCamelCase	:     str       =       {layer['module']: str(lowercase    ) for i, layer in enumerate(self._backbone.feature_info.info    )}
							super()._init_backbone(lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							requires_backends(cls    , ['vision', 'timm']    )
							from ...models.timm_backbone import TimmBackboneConfig

							_lowerCamelCase	:     Optional[int]       =       kwargs.pop('config'    , TimmBackboneConfig()    )

							_lowerCamelCase	:     Any       =       kwargs.pop('use_timm_backbone'    , lowercase    )
							if not use_timm:
											raise ValueError('use_timm_backbone must be True for timm backbones'    )

							_lowerCamelCase	:     Union[str, Any]       =       kwargs.pop('num_channels'    , config.num_channels    )
							_lowerCamelCase	:     Tuple       =       kwargs.pop('features_only'    , config.features_only    )
							_lowerCamelCase	:     int       =       kwargs.pop('use_pretrained_backbone'    , config.use_pretrained_backbone    )
							_lowerCamelCase	:     Tuple       =       kwargs.pop('out_indices'    , config.out_indices    )
							_lowerCamelCase	:     Union[str, Any]       =       TimmBackboneConfig(
							    backbone=lowercase    , num_channels=lowercase    , features_only=lowercase    , use_pretrained_backbone=lowercase    , out_indices=lowercase    , )
							return super()._from_config(lowercase    , **lowercase    )
			def    A_       (    self    , lowercase    ):
							pass






			def    A_       (    self    , lowercase    , lowercase=None    , lowercase=None    , lowercase=None    , **lowercase    ):
							_lowerCamelCase	:     Optional[Any]       =       return_dict if return_dict is not None else self.config.use_return_dict
							_lowerCamelCase	:     Dict       =       (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							_lowerCamelCase	:     Dict       =       output_attentions if output_attentions is not None else self.config.output_attentions

							if output_attentions:
											raise ValueError('Cannot output attentions for timm backbones at the moment'    )

							if output_hidden_states:
											# We modify the return layers to include all the stages of the backbone
											_lowerCamelCase	:     Any       =       self._all_layers
											_lowerCamelCase	:     Optional[Any]       =       self._backbone(lowercase    , **lowercase    )
											_lowerCamelCase	:     Optional[int]       =       self._return_layers
											_lowerCamelCase	:     str       =       tuple(hidden_states[i] for i in self.out_indices    )
							else:
											_lowerCamelCase	:     Dict       =       self._backbone(lowercase    , **lowercase    )
											_lowerCamelCase	:     Optional[Any]       =       None

							_lowerCamelCase	:     str       =       tuple(lowercase    )
							_lowerCamelCase	:     Dict       =       tuple(lowercase    ) if hidden_states is not None else None

							if not return_dict:
											_lowerCamelCase	:     Optional[int]       =       (feature_maps,)
											if output_hidden_states:
															_lowerCamelCase	:     Union[str, Any]       =       output + (hidden_states,)
											return output

							return BackboneOutput(feature_maps=lowercase    , hidden_states=lowercase    , attentions=lowercase    )







"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort




@nightly
@require_onnxruntime
@require_torch_gpu
class 						__UpperCamelCase     (     unittest.TestCase						):
					@property
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Dict:
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)
					@property
					def        UpperCAmelCase__ (							self							:	int   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			ort.SessionOptions()
											lowerCAmelCase   :Dict						=			False
											return options
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       str:
											lowerCAmelCase   :Union[str, Any]						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo.png'   )
											lowerCAmelCase   :str						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'   )
											lowerCAmelCase   :List[Any]						=			load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'   )

											# using the PNDM scheduler by default
											lowerCAmelCase   :Tuple						=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'     , revision='onnx'     , safety_checker=UpperCAmelCase     , feature_extractor=UpperCAmelCase     , provider=self.gpu_provider     , sess_options=self.gpu_options     , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase   )

											lowerCAmelCase   :Optional[Any]						=			'A red cat sitting on a park bench'

											lowerCAmelCase   :Optional[Any]						=			np.random.RandomState(0   )
											lowerCAmelCase   :Union[str, Any]						=			pipe(
											    prompt=UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , strength=0.7_5     , guidance_scale=7.5     , num_inference_steps=15     , generator=UpperCAmelCase     , output_type='np'     , )

											lowerCAmelCase   :Optional[int]						=			output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image   ).max() < 1e-2